Image showing how Synth-X Quant AI's AI analysis engine processes market data
Data-Driven Decision Support

A data-driven decision-making platform that stays ahead of market changes

For those who have chosen a work style that is not restricted by location. Synth-X Quant AI is a decision-making support tool that incorporates the analytical methods used by professional investors into a format that can be used by individuals. You can base your decisions on verified data, not on gut feeling.

We convert the intersection of millions of price, volume, and news data into a single decision indicator.

Problem

Decision-making without drowning in a sea of information

Financial markets are constantly flooded with not only changes in stock prices and exchange rates, but also financial results announcements, economic indicators, news, and comments on social media. It is not easy for individuals to understand all of this and maintain calm judgment. Especially for those who work on asset management while having a day job, collecting information itself can be a huge burden.

In addition, when the market changes suddenly, people tend to make irrational decisions out of impatience and anxiety. Buying and selling based on emotions is a risk factor that cannot be overlooked when building long-term assets.

Core Technology

Analysis and backtesting with Synth-X engine

The Synth-X engine, which is the core of Synth-X Quant AI, has a "backtest" function that verifies the effectiveness of strategies using past price data. Before you actually invest your money, you can see numerically how much expected value a strategy has under certain conditions.

By calculating indicators such as expected value, winning rate, and maximum drawdown, we provide judgment materials based on past statistical trends rather than intuitive predictions. This is designed to suppress the dispersion of emotional judgments that tend to occur during sudden market changes.

  • Calculation of expected values and risk indicators based on past data
  • Backtest comparison of multiple scenarios with different conditions
  • Consistent evaluation criteria that does not involve emotional buying and selling decisions
How Synth-X Quant AI's Synth-X engine processes data and generates analysis results
Methodology

Transparency in the decision process

Synth-X Quant AI is designed not as a "black box" that only presents conclusions, but as a "glass box" that allows you to see the process that led to the suggestion. Data is converted into decision material through the following four steps.

STEP 01

data collection

Continuously incorporates market data from multiple sources, including prices, volumes, economic indicators, and news.

STEP 02

pattern recognition

Compare past price movements to detect statistically significant trends and anomalies.

STEP 03

risk adjustment

We add an evaluation to the detected trends, taking into account volatility and past loss width.

STEP 04

presentation of suggestions

We will output the numerical values that serve as the basis as well as concrete suggestions to help you make decisions.

Use cases

Utilization scenarios for remote workers

The premise of Synth-X Quant AI is to have professional-level analysis at your fingertips, no matter where you work. Actual usage scenarios are as follows.

Portfolio optimization

When regularly reviewing the allocation of your assets, you can adjust the allocation bias by referring to comparisons of expected values based on past data.

Early understanding of risks

The system detects stocks and markets where volatility has spiked, and can be used to check the impact on your assets in advance.

real-time alerts

By receiving notifications when abnormal price movements are detected, you will be less likely to miss important changes without constantly looking at the screen.

Get Started

Increase decision accuracy

It is possible to make evidence-based asset management decisions while continuing to live a life where you can work anywhere. First, check out what suggestions the Synth-X engine can add to your own operational policy with a free analysis.